@inproceedings{78765a2d34c34bb3b84ab7fc5c4e1fd9,
title = "Convex fused lasso denoising with non-convex regularization and its use for pulse detection",
abstract = "We propose a convex formulation of the fused lasso signal approximation problem consisting of non-convex penalty functions. The fused lasso signal model aims to estimate a sparse piecewise constant signal from a noisy observation. Originally, the 1 norm was used as a sparsity-inducing convex penalty function for the fused lasso signal approximation problem. However, the 1 norm underestimates signal values. Non-convex sparsity-inducing penalty functions better estimate signal values. In this paper, we show how to ensure the convexity of the fused lasso signal approximation problem with non-convex penalty functions. We further derive a computationally efficient algorithm using the majorization-minimization technique. We apply the proposed fused lasso method for the detection of pulses.",
keywords = "Sparse signal, fused lasso, non-convex regularization, pulse detection, total variation denoising",
author = "Ankit Parekh and Selesnick, {Ivan W.}",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE Signal Processing in Medicine and Biology Symposium ; Conference date: 12-12-2015",
year = "2016",
month = feb,
day = "11",
doi = "10.1109/SPMB.2015.7405474",
language = "English",
series = "2015 IEEE Signal Processing in Medicine and Biology Symposium - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2015 IEEE Signal Processing in Medicine and Biology Symposium - Proceedings",
address = "United States",
}